Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

The aim of this survey paper is to provide an account of some of the important developments
in the autoregressive conditional heteroskedasticity (ARCH) model since its inception in a …

1.1 Introduction Research into time series models of changing variance and covariance,
which I will collectively call volatility models, has exploded in the last ten years. This activity …

When I worked on my Introduction to Multiple Time Series Analysis (Lutk ̈ ̈-pohl (1991)), a
suitable textbook for this? eld was not available. Given the great importance these methods …

We provide a framework for integration of high–frequency intraday data into the
measurement, modeling, and forecasting of daily and lower frequency return volatilities and …

A complete theory for evaluating interval forecasts has not been worked out to date. Most of
the literature implicitly assumes homoskedastic errors even when this is clearly violated, and …

This paper provides a survey and review of the major econometric work on long memory
processes, fractional integration, and their applications in economics and finance. Some of …

Engle's ARCH model is extended to permit parametric specifications for conditional
dependence beyond the mean and variance. The suggestion is to model the conditional …

A new class of fractionally integrated GARCH and EGARCH models for characterizing
financial market volatility is discussed. Monte Carlo simulations illustrate the reliability of …

Although many of the models commonly used in empirical finance are linear, the nature of
financial data suggests that non-linear models are more appropriate for forecasting and …